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Title: A Lightweight Authentication Architecture for Unsupervised Internet of Things (IoT) in Smart Home Applications
Authors: Gamundani, Attlee, M
Keywords: Authentication
smart home
internet of things
Issue Date: Nov-2018
Publisher: Namibia University of Science and Technology
Citation: Gamundani, A. (2018). A Lightweight Authentication Architecture for Unsupervised Internet of Things (IoT) in Smart Home Applications. (Unpublished doctoral thesis). Namibia University of Science and Technology, Windhoek.
Abstract: The Smart Home environment is made up of different objects that have sensing capabilities and have the potential to interact with each other seamlessly. This brings a lot of convenience to the control and monitoring of the surroundings around the home environment. This reality is brought about as a result of the Internet of Things (IoT) phenomenon. The potential benefits presented by IoT technologies around the Smart Home environment can and are hampered by security issues that are yet to be resolved both at the perception layer and the transmission layer. A simulated Smart Home environment that modelled critical application requirements for Assisted Ambient Living (AAL) spaces and Energy Saving Solutions (ESS) was used to evaluate the proposed lightweight authentication architecture’s efficiency, which was tested against existing similar solutions around the same functionality. The lightweight authentication architecture presented in this submission was tested using the SCYTHER tool, which allowed verification, falsification and security testing by checking on various classes of attacks and possible architecture behaviour. The architecture turned out secure for tested threats, guided by the Dolev-Yao model. The contribution of this research, is its pragmatic approach to the security design for unsupervised constrained things. Key findings from this work highlight two important aspects for proper security advancement, which are identity management of things in the IoT space and the scalability of using agent based models to reduce resource demands at the device level. As an envisaged future relevance of this work, the vision of smart cities can be realised
Appears in Collections:Computer Sciences
Masters and PhD Theses
Theses and Dessertations

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